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# ai-code-audit-cli # AI Code Audit CLI
A CLI tool that validates AI-generated code for common issues, anti-patterns, and security vulnerabilities A CLI tool that validates AI-generated code for common issues, anti-patterns, and security vulnerabilities. It analyzes code produced by AI assistants (like GitHub Copilot, Claude Code, etc.) and generates quality reports with improvement suggestions and confidence scores.
![CI Status](https://7000pct.gitea.bloupla.net/api/badges/7000pctAUTO/ai-code-audit-cli/status.svg)
![Python Version](https://img.shields.io/badge/python-3.9%2B-blue)
![License](https://img.shields.io/badge/License-MIT-green)
## Features
- **Security Vulnerability Detection**: Detect hardcoded secrets, SQL injection risks, command injection, and other security issues
- **Code Smell Detection**: Identify code smells like unused imports, mutable defaults, complex functions, and poor error handling
- **Multi-language Support**: Analyze Python, JavaScript, and TypeScript code
- **Confidence Scoring**: Get a 0-100 score reflecting code quality and security posture
- **Multiple Output Formats**: Terminal output with Rich formatting, JSON, and Markdown export options
- **Severity-based Filtering**: Filter issues by severity level (critical, high, medium, low, info)
## Installation
### From Source
```bash
git clone https://7000pct.gitea.bloupla.net/7000pctAUTO/ai-code-audit-cli.git
cd ai-code-audit-cli
pip install -e .
```
### Using pip
```bash
pip install ai-code-audit-cli
```
## Quick Start
```bash
# Audit a single file
audit scan path/to/file.py
# Audit a directory recursively
audit scan path/to/project/
# Audit with JSON output
audit scan path/to/code/ --format json
# Audit with verbose output
audit scan path/to/code/ --verbose
# Filter by language
audit scan path/to/code/ --language python
# Filter by severity
audit scan path/to/code/ --severity high
```
## Usage
### Basic Scanning
```bash
# Scan a Python file
audit scan my_script.py
# Scan a JavaScript file
audit scan index.js
# Scan a TypeScript file
audit scan app.ts
# Scan a directory
audit scan src/
```
### Output Options
```bash
# Rich terminal output (default)
audit scan path/to/code/
# JSON output for CI/CD
audit scan path/to/code/ --format json
# Markdown report
audit scan path/to/code/ --format markdown
# Quiet mode (minimal output)
audit scan path/to/code/ --quiet
```
### Filtering Options
```bash
# Only scan Python files
audit scan path/to/code/ --language python
# Only show critical and high severity issues
audit scan path/to/code/ --severity critical high
# Skip color output
audit scan path/to/code/ --no-color
# Verbose output with more details
audit scan path/to/code/ --verbose
```
## Configuration
### Audit Configuration File
Create an `audit.toml` file in your project root:
```toml
[severity]
critical = true
high = true
medium = true
low = true
info = false
[languages]
python = true
javascript = true
typescript = true
[scanners]
bandit = true
ruff = true
tree_sitter = true
[confidence]
min_score = 70
```
### PyProject.toml Integration
The tool automatically reads Ruff configuration from your `pyproject.toml`:
```toml
[tool.ruff]
line-length = 88
select = ["E", "F", "W", "C90", "I", "N", "UP", "B", "SIM"]
ignore = ["E501"]
[tool.ruff.per-file-ignores]
"__init__.py" = ["F401"]
```
## Output Formats
### Terminal Output
The default terminal output uses Rich formatting to display:
```
AI Code Audit Report
Summary:
┌─────────────────────────────────────────────┐
│ Files Scanned │ 15 │
│ Issues Found │ 23 │
│ Confidence Score │ 78 │
└─────────────────────────────────────────────┘
Issues by Severity:
├─ Critical: 2
├─ High: 5
├─ Medium: 8
├─ Low: 6
└─ Info: 2
```
### JSON Output
```json
{
"summary": {
"files_scanned": 15,
"total_issues": 23,
"confidence_score": 78
},
"issues": [
{
"id": "B001",
"severity": "high",
"category": "security",
"message": "Possible hardcoded password detected",
"file": "src/auth.py",
"line": 42
}
]
}
```
### Markdown Output
```markdown
# AI Code Audit Report
## Summary
| Metric | Value |
|--------|-------|
| Files Scanned | 15 |
| Issues Found | 23 |
| Confidence Score | 78 |
## Issues
### Critical
| File | Line | Issue |
|------|------|-------|
| src/auth.py | 42 | Possible hardcoded password |
```
## Confidence Score
The confidence score (0-100) reflects the overall quality and security posture of the analyzed code:
- **90-100**: Excellent - Code is well-structured and secure
- **70-89**: Good - Minor issues found, generally safe to use
- **50-69**: Moderate - Several issues, review recommended
- **30-49**: Poor - Significant issues, refactoring advised
- **0-29**: Critical - Major problems, do not deploy
### Score Calculation
The score is calculated based on:
1. **Security Issues (40% weight)**: Critical issues have highest impact
2. **Code Smells (30% weight)**: Anti-patterns and poor practices
3. **Complexity (20% weight)**: Function complexity and file size
4. **Error Handling (10% weight)**: Missing exception handling
## Supported Languages
| Language | Security Scanning | Linting | Pattern Detection |
|----------|------------------|---------|-------------------|
| Python | ✅ Bandit | ✅ Ruff | ✅ Tree-sitter |
| JavaScript | ⚠️ Basic | ✅ Ruff | ✅ Tree-sitter |
| TypeScript | ⚠️ Basic | ✅ Ruff | ✅ Tree-sitter |
## Scanner Details
### Bandit Scanner
Detects common security issues in Python code:
- Hardcoded passwords and secrets
- SQL injection vulnerabilities
- Command injection risks
- Insecure cryptographic usage
- Path traversal issues
### Ruff Scanner
Fast linting for code quality:
- PEP 8 compliance
- Import sorting
- Code complexity
- Anti-patterns
- Unused code
### Tree-sitter Scanner
Multi-language pattern detection:
- API key and credential patterns
- SQL injection patterns
- Dangerous function usage
- Deprecated API calls
## API Usage
### Python API
```python
from src.core.scanner import Scanner
from src.reporting.confidence import ConfidenceScorer
# Initialize scanner
scanner = Scanner()
# Scan a directory
results = scanner.scan_directory("path/to/code")
# Calculate confidence score
scorer = ConfidenceScorer()
score = scorer.calculate(results)
# Print results
scanner.print_report(results)
```
### Custom Configuration
```python
from src.core.config import AuditConfig
config = AuditConfig(
severity_filter={"critical", "high", "medium"},
language_filter={"python"},
scanners={"bandit", "ruff"}
)
scanner = Scanner(config=config)
results = scanner.scan("path/to/code")
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
### Development Setup
```bash
git clone https://7000pct.gitea.bloupla.net/7000pctAUTO/ai-code-audit-cli.git
cd ai-code-audit-cli
pip install -e ".[dev]"
# Run tests
pytest tests/ -v --cov=src
# Run linting
ruff check .
# Type checking
mypy src/
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- [Bandit](https://bandit.readthedocs.io/) for Python security scanning
- [Ruff](https://github.com/astral-sh/ruff) for fast linting
- [Tree-sitter](https://tree-sitter.github.io/tree-sitter/) for code parsing
- [Typer](https://typer.tiangolo.com/) for CLI interface
- [Rich](https://rich.readthedocs.io/) for beautiful terminal output